| dc.description.abstract | Millennium Villages Project is an initiative that is meant to demonstrate that the millennium
development goals could be achieved in an integrated approach in putting a combination of
interventions in place. Among these interventions are the health interventions of reducing the
prevalence of common diseases. Malaria is one of these common diseases. In 2005 Sauri
Millennium Village was started in western Kenya which was then followed by 13 other villages
across Africa. A baseline study was done in 2005 to measure the bench marks of the millennium
development goals indicators in the village. As part of these surveys, blood data was collected to
estimate the baseline prevalence of malaria in the Sauri Millennium village. This data was linked
to socioeconomic data to study factors affecting malaria prevalence. Malaria affects individuals
who are clustered in households and villages. In addition to individual effects, households and
villages have characteristics that influence malaria prevalence. The individual characteristics
under study were age and gender. The household characteristics were income and the education
status of the household the individual belongs. The village level factors were the counts of water
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bodies and the area covered by woods of the villages. Logistic regression models were applied to
understand the determinants of malaria. Considering the multilevel structure of data, the analysis
goes beyond the single-level modelling and explores the value of multilevel modelling in
understanding the malaria risk factors. The analysis showed that malaria prevalence among the
population at baseline was about 50% and was similar for males and females. The results also
showed that malaria prevalence decreases with age. Income and education status of the
households were also found to have an effect on malaria prevalence. The utility of the multilevel
techniques in answering the research questions clearly demonstrated the value of statistical
techniques in understanding factors affecting health outcomes. The recognition of complex
structures of data in statistical modelling processes, yield reliable results that help health
strategists make informed decisions in taming malaria. | en_US |
